AIMC Topic: Prefrontal Cortex

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Orthogonal representations for robust context-dependent task performance in brains and neural networks.

Neuron
How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this context-dependent decision-making problem, which trade o...

Brain oscillatory correlates of visuomotor adaptive learning.

NeuroImage
Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learn...

Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.

Scientific reports
A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key ...

Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State.

Frontiers in neural circuits
In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subser...

Meta-control: From psychology to computational neuroscience.

Cognitive, affective & behavioral neuroscience
Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself remain poorly understood. Following the terminology from ar...

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

International journal of molecular sciences
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as bi...

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials.

NeuroImage
The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field...

Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks.

Nature neuroscience
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timesc...

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

PloS one
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy ...

Identification of contributing genes of Huntington's disease by machine learning.

BMC medical genomics
BACKGROUND: Huntington's disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is s...